We have constructed neural networks and shown that these neural networks can predict the information-theoretically secure key rates of homodyne detection discrete-modulated CV-QKD with a great probability (up to \(99.2\%\) ) at a distance of \(0-100\)  km and an excess noise of no more than \(0.015\) . In particular, with the excess noise up to \(0.008\)  or more, the speed of our method is at least improved by six orders of magnitude compared to that of the numerical method in Refs. [1]}, [2]}. For example, it takes an average of 190 seconds to numerically calculate the point with the excess noise \(\xi \)  around \(0.008\) , which greatly affects the efficiency of QKD systems to calculate the secure key rate. In contrast, the neural networks can calculate tens of thousands of key rates in one second. Considering that it takes a certain amount of time for QKD system to collect data, the speed of predicting the key rates by the neural networks completely meets practical applications. This advantage brings us one step closer to achieving low latency for discrete modulated CV-QKD on a low-power platform.
